Our method builds upon previous Medical Out-of-Distribution (MOOD) challenge
winners that empirically show that synthetic local anomalies generated copying
/ interpolating foreign patches are useful to train segmentation networks able
to generalize to unseen types of anomalies. In terms of the synthetic anomaly
generation process, our contributions makes synthetic anomalies more
heterogeneous and challenging by 1) using random shapes instead of squares and
2) smoothing the interpolation edge of anomalies so networks cannot rely on the
high gradient between image - foreign patch to identify anomalies. Our
experiments using the validation set of 2020 MOOD winners show that both
contributions improved substantially the method performance. We used a standard
3D U-Net architecture as segmentation network, trained patch-wise in both brain
and abdominal datasets. Our final challenge submission consisted of 10 U-Nets
trained across 5 data folds with different configurations of the anomaly
generation process. Our method achieved first position in both sample-wise and
pixel-wise tasks in the 2022 edition of the Medical Out-of-Distribution held at
MICCAI.

我们的研究基于之前的医学领域离群分布挑战（MOOD）的获奖者的工作，其经验性地表明通过复制和插值外来补丁生成合成局部异常有助于训练分割网络，从而能够推广到未见过的异常类型。我们的贡献在合成异常生成过程中，通过使用随机形状而不是方形，并平滑异常的插值边缘，使合成异常更加多样化和具有挑战性，使网络无法仅仅依赖图像 - 外来补丁之间的高梯度来识别异常。我们的实验证明，这两项贡献显著改进了该方法的性能。我们使用标准的 3D U-Net 架构作为分割网络，在脑部和腹部数据集上以补丁方式进行训练。我们最终的挑战提交包括 10 个在不同异常生成过程配置下训练的 U-Net，通过 5 个数据折叠进行训练。我们的方法在 MICCAI 举办的 2022 年医学离群分布挑战的样本级和像素级任务中取得了第一名。